SynthMoDe / README.md
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---
dataset_info:
features:
- name: filename
dtype: string
- name: image1
dtype: image
- name: image2
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 59881425.25
num_examples: 2043
download_size: 56257629
dataset_size: 59881425.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- translation
language:
- mr
---
# SynthMoDe: Synthetic Modi Script to Devanagari Transliteration Dataset
This dataset supports research in the transliteration of **Modi script**—an ancient Indian script historically used in Maharashtra—into **Devanagari**, the script currently used for Marathi and several other Indian languages.
The dataset was introduced in the following paper:
> **Historic Scripts to Modern Vision: A Novel Dataset and A VLM Framework for Transliteration of Modi Script to Devanagari**
> *Harshal Kausadikar, Tanvi Kale, Onkar Susladkar, Sparsh Mittal*
> **Accepted at ICDAR 2025**
> [arXiv:2503.13060v2](https://arxiv.org/abs/2503.13060v2)
## Dataset Features
- **Modi script image patches**
- **Aligned Devanagari transliterations**
- Covers a diverse range of historical Modi handwriting styles
- Suitable for OCR, script transliteration, vision-language modeling, and low-resource script research
## Usage
This dataset is ideal for:
- Handwritten script recognition and transliteration
- Vision-language model (VLM) training for historical scripts
- Document digitization and cultural heritage preservation
## MoDeTrans Dataset
The SynthMode Dataset is created from the ModeTrans Dataset that is available at: https://huggingface.co/datasets/historyHulk/MoDeTrans
## Citation
If you use this dataset in your research or publications, **please cite the following paper**:
```bibtex
@article{kausadikar2025historic,
title={Historic Scripts to Modern Vision: A Novel Dataset and A VLM Framework for Transliteration of Modi Script to Devanagari},
author={Kausadikar, Harshal and Kale, Tanvi and Susladkar, Onkar and Mittal, Sparsh},
journal={arXiv preprint arXiv:2503.13060},
year={2025},
note={Accepted at the 19th International Conference on Document Analysis and Recognition (ICDAR 2025)}
}